Problem 2. Assume the following dataset is given: {(2, 2), (4, 4), (5, 5), (6,6), (8,8), (9,9), (0,4), (4, 0)}.

Apply the k-means clustering method for k = 4 on this dataset. Use Manhattan distance as the distance

function to compute distances between centroids and objects in the dataset. Moreover, Lets assume the

initial clusters C1, C2, C3, and CA are as follows:

C1: {(2,2), (4,4), (6, 6)}

C2: {(0,4), (4, 0)}

C3: {(5,5), (9,9)}

CA: { (8,8)}

* You can stop after three iterations if the algorithm does not converge earlier.

Problem 2. Assume the following dataset is given: {(2,2),(4,4), (5,5), (6,6), (8,8), (9,9),(0,4),(4,0)). Apply the k-means clustering method for k = 4 on this dataset. Use Manhattan distance as the distance function to compute distances between centroids and objects in the dataset. Moreover, Lets assume the initial clusters C1,C2,C3, and C are as follows: C: {(2,2), (4,4), (6,6)} C2 = {(0,4),(4,0)} Cz: {(5,5), (9,9)} CA : {(8,8)) * You can stop after three iterations if the algorithm does not converge earlier